Machine learning based solar photovoltaic power forecasting: a review and comparison
J Gaboitaolelwe, AM Zungeru, A Yahya… - IEEE …, 2023 - ieeexplore.ieee.org
The growing interest in renewable energy and the falling prices of solar panels place solar
electricity in a favourable position for adoption. However, the high-rate adoption of …
electricity in a favourable position for adoption. However, the high-rate adoption of …
Machine learning approaches to predict electricity production from renewable energy sources
Bearing in mind European Green Deal assumptions regarding a significant reduction of
green house emissions, electricity generation from Renewable Energy Sources (RES) is …
green house emissions, electricity generation from Renewable Energy Sources (RES) is …
Bayesian hyperparameter optimization of stacked bidirectional long short-term memory neural network for the state of charge estimation
The battery industry has recently grown as a result of electric power adoption in many
applications showing less reliance on fossil fuels. Accurate estimation of the State of Charge …
applications showing less reliance on fossil fuels. Accurate estimation of the State of Charge …
Memory long and short term time series network for ultra-short-term photovoltaic power forecasting
C Huang, M Yang - Energy, 2023 - Elsevier
Photovoltaic (PV) power is stochastic, intermittent and volatile, which has brought huge
challenges to the safe and stable operation of the power grid. Accurate PV power …
challenges to the safe and stable operation of the power grid. Accurate PV power …
Deep graph gated recurrent unit network-based spatial–temporal multi-task learning for intelligent information fusion of multiple sites with application in short-term …
M Bai, Z Zhou, J Li, Y Chen, J Liu, X Zhao… - Expert Systems with …, 2024 - Elsevier
Accurate photovoltaic (PV) power forecast is crucial for carbon neutrality. Current researches
on PV power forecast mainly focus on using temporal information from single PV station, and …
on PV power forecast mainly focus on using temporal information from single PV station, and …
Assessment of different deep learning methods of power generation forecasting for solar PV system
WC Kuo, CH Chen, SH Hua, CC Wang - Applied Sciences, 2022 - mdpi.com
An increase in renewable energy injected into the power system will directly cause a
fluctuation in the overall voltage and frequency of the power system. Thus, renewable …
fluctuation in the overall voltage and frequency of the power system. Thus, renewable …
[HTML][HTML] Advancing renewable energy forecasting: A comprehensive review of renewable energy forecasting methods
Socioeconomic growth and population increase are driving a constant global demand for
energy. Renewable energy is emerging as a leading solution to minimise the use of fossil …
energy. Renewable energy is emerging as a leading solution to minimise the use of fossil …
Short-term day-ahead photovoltaic output forecasting using PCA-SFLA-GRNN algorithm
The work of forecasting solar power is becoming more crucial with directives to regulate the
quality of the power and increase the system's reliability as photovoltaic (PV) sites are being …
quality of the power and increase the system's reliability as photovoltaic (PV) sites are being …
Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting
A novel Variational Mode Decomposition (VMD) combined Fuzzy-Twin Support Vector
Machine Model with deep learning mechanism is devised in this research study to forecast …
Machine Model with deep learning mechanism is devised in this research study to forecast …
[HTML][HTML] Trends and challenges of the interactions between microclimate and electric power systems
The increasing penetration of renewables has made electric power systems meteorology-
sensitive. Meteorology has become one of the decisive factors and the key source of …
sensitive. Meteorology has become one of the decisive factors and the key source of …